Oil Spill Hyperspectral Remote Sensing Detection Based on DCNN with Multi-Scale Features

doi:10.2112/SI90-042.1

	Oil Spill Hyperspectral Remote Sensing Detection Based on DCNN with Multi-Scale Features
	Yang, Jun-Fang 1; Wan, Jian-Hua 1; Ma, Yi 2; Zhang, Jie 2; Hu, Ya-Bin 3; Jiang, Zong-Chen 4
刊名	JOURNAL OF COASTAL RESEARCH
	2019-06
页码	332-339
关键词	Oil spill hyperspectral imagery DCNN multi-scale features remote sensing
ISSN号	0749-0208
DOI	10.2112/SI90-042.1
英文摘要	In this paper, a deep convolutional neural network (DCNN) model is developed for sea surface oil spill accurate detection using multi-scale features with AISA+ airborne hyperspectral remote sensing image. Based on multi-scale features after wavelet transform (WT), a deep convolution neural network classification algorithm with seven-layer network structure is proposed to detect oil spill of the Penglai 19-3C platform in 2011, and the accuracy evaluation is conducted on the overall situation. The detection results of proposed method are compared with those of the classical SVM, RF and DBN method. The results show that the accuracies of DCNN for oil spill detection based on different-scale features are all more than 85 %, which are much better than those of SVM, RF and DBN method, and the detection results can maintain the continuity of oil film at sea. Among them, the detection result of DCNN model based on spectral feature information combined with low-frequency component of 1-level wavelet transform has the best effect and highest detection accuracy, reaching 87.51 %.
资助项目	National Natural Science Foundation of China[61890964] ; National Natural Science Foundation of China[41706208]
WOS关键词	CLASSIFICATION ; SATELLITE ; IMAGERY ; THICKNESS
WOS研究方向	Environmental Sciences & Ecology ; Physical Geography ; Geology
语种	英语
出版者	COASTAL EDUCATION & RESEARCH FOUNDATION
WOS记录号	WOS:000485714500043
内容类型	期刊论文
源URL	[http://ir.fio.com.cn:8080/handle/2SI8HI0U/30212]
专题	自然资源部第一海洋研究所
通讯作者	Ma, Yi
作者单位	1.China Univ Petr, Sch Geosci, Qingdao, Shandong, Peoples R China 2.Minist Nat Resources, Inst Oceanog 1, Marine Phys & Remote Sensing Res Dept, Qingdao, Shandong, Peoples R China 3.Dalian Maritime Univ, Informat Sci & Technol Coll, Dalian, Peoples R China 4.Shandong Univ Sci & Technol, Coll Surveying & Mapping Sci & Engn, Qingdao, Shandong, Peoples R China
推荐引用方式 GB/T 7714	Yang, Jun-Fang,Wan, Jian-Hua,Ma, Yi,et al. Oil Spill Hyperspectral Remote Sensing Detection Based on DCNN with Multi-Scale Features[J]. JOURNAL OF COASTAL RESEARCH,2019:332-339.
APA	Yang, Jun-Fang,Wan, Jian-Hua,Ma, Yi,Zhang, Jie,Hu, Ya-Bin,&Jiang, Zong-Chen.(2019).Oil Spill Hyperspectral Remote Sensing Detection Based on DCNN with Multi-Scale Features.JOURNAL OF COASTAL RESEARCH,332-339.
MLA	Yang, Jun-Fang,et al."Oil Spill Hyperspectral Remote Sensing Detection Based on DCNN with Multi-Scale Features".JOURNAL OF COASTAL RESEARCH (2019):332-339.